diff --git a/Pipfile b/Pipfile
index a95b9fa..ac82add 100644
--- a/Pipfile
+++ b/Pipfile
@@ -4,12 +4,23 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
-langchain = "*"
 python-dotenv = "*"
 langsmith = "*"
 openai = "*"
-chromadb = "*"
 pypdf = "*"
+duckduckgo-search = "*"
+requests = "*"
+beautifulsoup4 = "*"
+langserve = "*"
+sse-starlette = "*"
+langchain = "*"
+docarray = "==0.39.1"
+pydantic = "==1.10.13"
+azure-search-documents = "==11.4.0b8"
+azure-identity = "==1.15.0"
+chromadb = "*"
+llama-index = "*"
+traceloop-sdk = "*"
 
 [dev-packages]
 ipykernel = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index bc24798..e90061e 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "298a87d038929b30b06e97e2340b05aeaa0b8c141f78e8dcb8c4188aaf4c10fe"
+            "sha256": "73f948d81eaf2eb20447276a2766555eae570de2657c93a9653597dfc0182517"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -18,85 +18,85 @@
     "default": {
         "aiohttp": {
             "hashes": [
-                "sha256:05857848da443c8c12110d99285d499b4e84d59918a21132e45c3f0804876994",
-                "sha256:05a183f1978802588711aed0dea31e697d760ce9055292db9dc1604daa9a8ded",
-                "sha256:09f23292d29135025e19e8ff4f0a68df078fe4ee013bca0105b2e803989de92d",
-                "sha256:11ca808f9a6b63485059f5f6e164ef7ec826483c1212a44f268b3653c91237d8",
-                "sha256:1736d87dad8ef46a8ec9cddd349fa9f7bd3a064c47dd6469c0d6763d3d49a4fc",
-                "sha256:1df43596b826022b14998f0460926ce261544fedefe0d2f653e1b20f49e96454",
-                "sha256:23170247ef89ffa842a02bbfdc425028574d9e010611659abeb24d890bc53bb8",
-                "sha256:2779f5e7c70f7b421915fd47db332c81de365678180a9f3ab404088f87ba5ff9",
-                "sha256:28185e36a78d247c55e9fbea2332d16aefa14c5276a582ce7a896231c6b1c208",
-                "sha256:2cbc14a13fb6b42d344e4f27746a4b03a2cb0c1c3c5b932b0d6ad8881aa390e3",
-                "sha256:2d71abc15ff7047412ef26bf812dfc8d0d1020d664617f4913df2df469f26b76",
-                "sha256:2d820162c8c2bdbe97d328cd4f417c955ca370027dce593345e437b2e9ffdc4d",
-                "sha256:317719d7f824eba55857fe0729363af58e27c066c731bc62cd97bc9c3d9c7ea4",
-                "sha256:35a68cd63ca6aaef5707888f17a70c36efe62b099a4e853d33dc2e9872125be8",
-                "sha256:3607375053df58ed6f23903aa10cf3112b1240e8c799d243bbad0f7be0666986",
-                "sha256:366bc870d7ac61726f32a489fbe3d1d8876e87506870be66b01aeb84389e967e",
-                "sha256:3abf0551874fecf95f93b58f25ef4fc9a250669a2257753f38f8f592db85ddea",
-                "sha256:3d7f6235c7475658acfc1769d968e07ab585c79f6ca438ddfecaa9a08006aee2",
-                "sha256:3dd8119752dd30dd7bca7d4bc2a92a59be6a003e4e5c2cf7e248b89751b8f4b7",
-                "sha256:42fe4fd9f0dfcc7be4248c162d8056f1d51a04c60e53366b0098d1267c4c9da8",
-                "sha256:45820ddbb276113ead8d4907a7802adb77548087ff5465d5c554f9aa3928ae7d",
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-                "sha256:4b777c9286b6c6a94f50ddb3a6e730deec327e9e2256cb08b5530db0f7d40fd8",
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+                "sha256:bff7e2811814fa2271be95ab6e84c9436d027a0e59665de60edf44e529a42c1f",
+                "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0",
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+                "sha256:c9110c06eaaac7e1f5562caf481f18ccf8f6fdf4c3323feab28a93d34cc646bd",
+                "sha256:ca7ca5abfbfe8d39e653870fbe8d7710be7a857f8a8386fc9de1aae2e02ce7e4",
+                "sha256:cae4c0c2ca800c793cae07ef3d40794625471040a87e1ba392039639ad61ab5b",
+                "sha256:cdefe289681507187e375a5064c7599f52c40343a8701761c802c1853a504558",
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+                "sha256:d737e69d193dac7296365a6dcb73bbbf53bb760ab25a3727716bbd42022e8d7a",
+                "sha256:d7481f581251bb5558ba9f635db70908819caa221fc79ee52a7f58392778c636",
+                "sha256:df9cf74b9bc03d586fc53ba470828d7b77ce51b0582d1d0b5b2fb673c0baa32d",
+                "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590",
+                "sha256:ecca113f19d5e74048c001934045a2b9368d77b0b17691d905af18bd1c21275e",
+                "sha256:ee2527134f95e106cc1653e9ac78846f3a2ec1004cf20ef4e02038035a74544d",
+                "sha256:f27fdaadce22f2ef950fc10dcdf8048407c3b42b73779e48a4e76b3c35bca26c",
+                "sha256:f694dc8a6a3112059258a725a4ebe9acac5fe62f11c77ac4dcf896edfa78ca28",
+                "sha256:f800164276eec54e0af5c99feb9494c295118fc10a11b997bbb1348ba1a52065",
+                "sha256:ffcd828e37dc219a72c9012ec44ad2e7e3066bec6ff3aaa19e7d435dbf4032ca"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==3.9.0"
+            "version": "==3.9.1"
         },
         "aiosignal": {
             "hashes": [
@@ -106,14 +106,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.3.1"
         },
-        "annotated-types": {
-            "hashes": [
-                "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43",
-                "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.6.0"
-        },
         "anyio": {
             "hashes": [
                 "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780",
@@ -122,6 +114,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==3.7.1"
         },
+        "asgiref": {
+            "hashes": [
+                "sha256:89b2ef2247e3b562a16eef663bc0e2e703ec6468e2fa8a5cd61cd449786d4f6e",
+                "sha256:9e0ce3aa93a819ba5b45120216b23878cf6e8525eb3848653452b4192b92afed"
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+            "markers": "python_version >= '3.7'",
+            "version": "==3.7.2"
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                 "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04",
@@ -130,6 +130,37 @@
             "markers": "python_version >= '3.7'",
             "version": "==23.1.0"
         },
+        "azure-common": {
+            "hashes": [
+                "sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3",
+                "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"
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+            "version": "==1.1.28"
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+        "azure-core": {
+            "hashes": [
+                "sha256:13b485252ecd9384ae624894fe51cfa6220966207264c360beada239f88b738a",
+                "sha256:604a005bce6a49ba661bb7b2be84a9b169047e52fcfcd0a4e4770affab4178f7"
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+            "markers": "python_version >= '3.7'",
+            "version": "==1.29.6"
+        },
+        "azure-identity": {
+            "hashes": [
+                "sha256:4c28fc246b7f9265610eb5261d65931183d019a23d4b0e99357facb2e6c227c8",
+                "sha256:a14b1f01c7036f11f148f22cd8c16e05035293d714458d6b44ddf534d93eb912"
+            ],
+            "index": "pypi",
+            "version": "==1.15.0"
+        },
+        "azure-search-documents": {
+            "hashes": [
+                "sha256:4137daa2db75bff9484d394c16c0604822a51281cad2f50e11d7c48dd8d4b4cf",
+                "sha256:b178ff52918590191a9cb7f411a9ab3cb517663666a501a3e84b715d19b0d93b"
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+            "index": "pypi",
+            "version": "==11.4.0b8"
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         "backoff": {
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                 "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba",
@@ -140,30 +171,44 @@
         },
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-            "version": "==4.0.1"
+            "markers": "python_version >= '3.7'",
+            "version": "==4.1.2"
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@@ -181,6 +226,64 @@
             "markers": "python_version >= '3.6'",
             "version": "==2023.11.17"
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@@ -309,11 +412,11 @@
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@@ -339,13 +442,54 @@
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             "version": "==15.0.1"
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@@ -363,13 +507,29 @@
             "markers": "python_version >= '3.6'",
             "version": "==1.8.0"
         },
+        "docarray": {
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@@ -659,19 +836,19 @@
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@@ -683,19 +860,19 @@
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@@ -705,6 +882,21 @@
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@@ -730,21 +922,158 @@
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@@ -768,6 +1174,22 @@
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@@ -856,6 +1278,22 @@
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@@ -895,7 +1333,7 @@
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@@ -908,88 +1346,176 @@
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@@ -1007,12 +1533,51 @@
             "markers": "python_version >= '3.7'",
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@@ -1068,11 +1633,11 @@
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@@ -1082,132 +1647,81 @@
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             "version": "==0.3.0"
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@@ -1311,6 +1946,14 @@
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@@ -1319,6 +1962,14 @@
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@@ -1335,6 +1986,14 @@
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@@ -1390,6 +2049,14 @@
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@@ -1414,6 +2081,48 @@
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@@ -1534,13 +2243,28 @@
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-                "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"
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             "markers": "python_version >= '3.8'",
-            "version": "==4.8.0"
+            "version": "==4.9.0"
         },
         "typing-inspect": {
             "hashes": [
@@ -1549,6 +2273,14 @@
             ],
             "version": "==0.9.0"
         },
+        "tzdata": {
+            "hashes": [
+                "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a",
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+            "markers": "python_version >= '2'",
+            "version": "==2023.3"
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                 "sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07",
@@ -1650,11 +2382,11 @@
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@@ -1964,19 +2712,19 @@
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@@ -1996,11 +2744,11 @@
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@@ -2036,41 +2784,41 @@
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+                "sha256:cc69949484171cc961e6ecd4a8911b9ce7a0d1f738fcae717177c231bf77437b",
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+                "sha256:e10a4b5a4b1192d74853cc71a5e9fd022594573926c2a3a4802020360aa719d8",
+                "sha256:e624c789359f1a16f83f35e2c705d07663ff2b4d4479bad35621178d8f0f6ea4",
+                "sha256:e690145a8c0c273c28d3b89d6fb32c45e0d9605b2293c10e650265bf5c11cfec",
+                "sha256:ea1608dd169da230a0ad602d5b1ebd39807ac96cae1845c3ceed39af08a5c6df",
+                "sha256:ea253b368eb41116011add00f8d5726762320b1bda892f744c91997b65754d73",
+                "sha256:eb7e49a17fb8c77d3119d41a4523e432eb0c6932187c37deb6fbb00cc3028088",
+                "sha256:ef12e259e7bc317c7597d4f6ef59b97b913e162d83b421dd0db3d6410f17a244",
+                "sha256:f8429b17cbb746c3e043cb986328da023657e79d5ed258b711c06a70c2ea7537",
+                "sha256:fa99973d2ed20417744fca0073390ad65ce225b546febb0580358e36aa90dba6",
+                "sha256:faf79a302f834d9e8304fafdc11d0d042266667ac45209afa57e5efc998e3872",
+                "sha256:fc31baa0c32a2ca660784d5af3b9487e13b61b3032cb01a115fce6588e1bed30"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==25.1.1"
+            "version": "==25.1.2"
         },
         "six": {
             "hashes": [
@@ -2231,35 +2979,35 @@
         },
         "tornado": {
             "hashes": [
-                "sha256:1bd19ca6c16882e4d37368e0152f99c099bad93e0950ce55e71daed74045908f",
-                "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5",
-                "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d",
-                "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3",
-                "sha256:71a8db65160a3c55d61839b7302a9a400074c9c753040455494e2af74e2501f2",
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-                "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16",
-                "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a",
-                "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17",
-                "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0",
-                "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"
+                "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0",
+                "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63",
+                "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263",
+                "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052",
+                "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f",
+                "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee",
+                "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78",
+                "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579",
+                "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212",
+                "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e",
+                "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==6.3.3"
+            "version": "==6.4"
         },
         "traitlets": {
             "hashes": [
-                "sha256:9b232b9430c8f57288c1024b34a8f0251ddcc47268927367a0dd3eeaca40deb5",
-                "sha256:baf991e61542da48fe8aef8b779a9ea0aa38d8a54166ee250d5af5ecf4486619"
+                "sha256:f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33",
+                "sha256:fcdaa8ac49c04dfa0ed3ee3384ef6dfdb5d6f3741502be247279407679296772"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==5.13.0"
+            "version": "==5.14.0"
         },
         "wcwidth": {
             "hashes": [
-                "sha256:390c7454101092a6a5e43baad8f83de615463af459201709556b6e4b1c861f97",
-                "sha256:aec5179002dd0f0d40c456026e74a729661c9d468e1ed64405e3a6c2176ca36f"
+                "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02",
+                "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"
             ],
-            "version": "==0.2.10"
+            "version": "==0.2.12"
         }
     }
 }
diff --git a/analysis/main_bing_search.py b/analysis/main_bing_search.py
new file mode 100644
index 0000000..e53be2d
--- /dev/null
+++ b/analysis/main_bing_search.py
@@ -0,0 +1,160 @@
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+import requests
+from bs4 import BeautifulSoup
+from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
+import os
+from dotenv import load_dotenv
+from langchain.utilities import BingSearchAPIWrapper
+import json
+
+load_dotenv()
+
+
+RESULT_PER_QUESTION = 3
+
+bing_search = BingSearchAPIWrapper()
+
+
+def web_search(query: str, num_results: int = RESULT_PER_QUESTION):
+    results = bing_search.results(query, num_results)
+    return [r["link"] for r in results]
+
+
+SUMMARY_TEMPLATE = """{text}
+-----------
+Using above text, answer in short the following question:
+
+> {question}
+-----------
+
+if the question cannot be answered, imply summarize the text. Include all factual information, numbers, statistics, etc.
+"""
+
+SUMMARY_PROMPT = ChatPromptTemplate.from_template(SUMMARY_TEMPLATE)
+
+
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+
+def scrape_text(url: str):
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            soup = BeautifulSoup(response.text, "html.parser")
+            text = soup.get_text(separator="", strip=True)
+            return text
+        else:
+            return f"Failed to scrape text : {response.status_code}"
+
+    except Exception as e:
+        print(e)
+        return f"Failed to scrape text {e}"
+
+
+# get the content of the page and use it as the context
+# url = "https://blog.langchain.dev/announcing-langsmith/"
+scrape_and_summarize_chain = (
+    RunnablePassthrough.assign(text=lambda x: scrape_text(x["url"])[:10000])
+    | SUMMARY_PROMPT
+    | llm
+    | StrOutputParser()
+)
+
+web_search_chain = (
+    RunnablePassthrough.assign(urls=lambda x: web_search(x["question"]))
+    | (lambda x: [{"question": x["question"], "url": u} for u in x["urls"]])
+    | scrape_and_summarize_chain.map()
+)
+
+
+SEARCH_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        (
+            "user",
+            "Write 3 google search queries to search online that form an"
+            "objective opinion from the following: {question}\n",
+        ),
+        "You must respond with a list of strings in the following format:"
+        '["query1", "query2", "query3"]',
+    ]
+)
+
+search_question_chain = SEARCH_PROMPT | llm | StrOutputParser() | json.loads
+
+full_research_chain = (
+    search_question_chain
+    | (lambda x: [{"question": q} for q in x])
+    | web_search_chain.map()
+)
+
+
+WRITER_SYSTEM_PROMPT = "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text."
+
+# Report prompts from https://github.com/assafelovic/gpt-researcher/blob/master/gpt_researcher/master/prompts.py
+RESEARCH_REPORT_TEMPLATE = """Information:
+--------
+{research_summary}
+--------
+Using the above information, answer the following question or topic: "{question}" in a detailed report -- \
+The report should focus on the answer to the question, should be well structured, informative, \
+in depth, with facts and numbers if available and a minimum of 1,200 words.
+You should strive to write the report as long as you can using all relevant and necessary information provided.
+You must write the report with markdown syntax.
+You MUST determine your own concrete and valid opinion based on the given information. Do NOT deter to general and meaningless conclusions.
+Write all used source urls at the end of the report, and make sure to not add duplicated sources, but only one reference for each.
+You must write the report in apa format.
+Please do your best, this is very important to my career."""  # noqa: E501
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", WRITER_SYSTEM_PROMPT),
+        ("user", RESEARCH_REPORT_TEMPLATE),
+    ]
+)
+
+
+def collapse_list_of_lists(list_of_lists):
+    content = []
+    for l in list_of_lists:
+        content.append("\n\n".join(l))
+    return "\n\n".join(content)
+
+
+chain = (
+    RunnablePassthrough.assign(
+        research_summary=full_research_chain | collapse_list_of_lists
+    )
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+#####  LANGSERVE  #####
+#!/usr/bin/env python
+from fastapi import FastAPI
+from langserve import add_routes
+
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="A simple api server using Langchain's Runnable interfaces",
+)
+
+add_routes(
+    app,
+    chain,
+    path="/research-assistant",
+)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)
diff --git a/analysis/main_pdf_search.py b/analysis/main_pdf_search.py
new file mode 100644
index 0000000..8a4ef06
--- /dev/null
+++ b/analysis/main_pdf_search.py
@@ -0,0 +1,199 @@
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+import requests
+from bs4 import BeautifulSoup
+from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
+import os
+from dotenv import load_dotenv
+from langchain.utilities import BingSearchAPIWrapper
+from pypdf import PdfReader
+
+from langchain.text_splitter import TokenTextSplitter, RecursiveCharacterTextSplitter
+from langchain.vectorstores.chroma import Chroma
+
+import json
+
+load_dotenv()
+
+
+persist_directory = "data/chroma"
+pdf_path = "./docs/Perizia.pdf"
+collection_name = "perizia"
+
+# embedding
+embedding = AzureOpenAIEmbeddings(
+    deployment=os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
+    chunk_size=1,
+    embedding_ctx_length=1000,
+)
+# vector db
+vector_db = Chroma(
+    persist_directory=persist_directory,
+    embedding_function=embedding,
+    collection_name=collection_name,
+)
+
+
+def load_pdf(doc_path: str, embedding):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=400, chunk_overlap=15, separators=["\n\n", "\n", " "]
+    )
+    pdf = PdfReader(doc_path)
+
+    docs = []
+    for page_num in range(len(pdf.pages)):
+        pdf_page = pdf.pages[page_num]
+        pdf_page_text = pdf_page.extract_text()
+
+        # create metadata
+        # page_nr = int(page_num + 1)
+        # name_without_extension = file_name.split(".")[0]
+        # source = f"{name_without_extension}-page-{page_nr}.pdf"
+
+        # split page
+        text_splitted = text_splitter.split_text(pdf_page_text)
+        # metadatas = [{"source": source, "page": page_nr} for _ in text_splitted]
+
+        # convert to document
+        documents = text_splitter.create_documents(texts=text_splitted)
+        if len(documents) > 0:
+            docs.extend(documents)
+
+    db = vector_db.from_documents(
+        documents=docs,
+        embedding=embedding,
+        persist_directory=persist_directory,
+        collection_name=collection_name,
+    )
+
+
+# load_pdf(pdf_path, embedding)
+
+SUMMARY_TEMPLATE = """{text}
+-----------
+Using above text, answer in short the following question:
+
+> {question}
+-----------
+
+if the question cannot be answered, imply summarize the text. Include all factual information, numbers, statistics, etc.
+Use same language as the question.
+"""
+
+SUMMARY_PROMPT = ChatPromptTemplate.from_template(SUMMARY_TEMPLATE)
+
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+
+def doc_search(question: str):
+    search_result = vector_db.similarity_search(query=question)
+    return [doc.page_content for doc in search_result]
+
+
+doc_search_chain = (
+    RunnablePassthrough.assign(text=lambda x: doc_search(x["question"]))
+    | SUMMARY_PROMPT
+    | llm
+    # | (lambda x: [{"question": x["question"], "text": t} for t in x["texts"]])
+    | StrOutputParser()
+)
+
+
+SEARCH_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        (
+            "user",
+            "Write 3 search queries to search into my documents that form an"
+            "objective opinion from the following: {question}\n"
+            "Use the same language as the question.",
+        ),
+        "You must respond with a list of strings in the following format:"
+        '["query1", "query2", "query3"]',
+    ]
+)
+
+search_question_chain = SEARCH_PROMPT | llm | StrOutputParser() | json.loads
+
+full_research_chain = (
+    search_question_chain
+    | (lambda x: [{"question": q} for q in x])
+    | doc_search_chain.map()
+)
+
+WRITER_SYSTEM_PROMPT = "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text."
+
+# Report prompts from https://github.com/assafelovic/gpt-researcher/blob/master/gpt_researcher/master/prompts.py
+RESEARCH_REPORT_TEMPLATE = """Information:
+--------
+{research_summary}
+--------
+Using the above information, answer the following question or topic: "{question}" in a detailed report -- \
+The report should focus on the answer to the question, should be well structured, informative, \
+in depth, with facts and numbers if available and a minimum of 1,200 words.
+You should strive to write the report as long as you can using all relevant and necessary information provided.
+You must write the report with markdown syntax.
+You MUST determine your own concrete and valid opinion based on the given information. Do NOT deter to general and meaningless conclusions.
+Write all used source urls at the end of the report, and make sure to not add duplicated sources, but only one reference for each.
+You must write the report in apa format.
+Respond with the same language as the given question.
+Please do your best, this is very important to my career."""  # noqa: E501
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", WRITER_SYSTEM_PROMPT),
+        ("user", RESEARCH_REPORT_TEMPLATE),
+    ]
+)
+
+
+def collapse_list_of_lists(list_of_lists):
+    content = []
+    for l in list_of_lists:
+        content.append("\n\n".join(l))
+    return "\n\n".join(content)
+
+
+chain = (
+    RunnablePassthrough.assign(research_summary=full_research_chain)
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+
+# chain.invoke({"question": "What are Actors in Dapr ?"})
+
+# question = "question"
+# chain.invoke({"question": question})
+
+
+#####  LANGSERVE  #####
+RUN_LANGSERVE = True
+
+if RUN_LANGSERVE:
+    #!/usr/bin/env python
+    from fastapi import FastAPI
+    from langserve import add_routes
+
+    app = FastAPI(
+        title="LangChain Server",
+        version="1.0",
+        description="A simple api server using Langchain's Runnable interfaces",
+    )
+
+    add_routes(
+        app,
+        chain,
+        path="/research-assistant",
+    )
+
+    if __name__ == "__main__":
+        import uvicorn
+
+        uvicorn.run(app, host="localhost", port=8000)
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diff --git a/docs/Dapr-for-NET-Developers.pdf b/docs/Dapr-for-NET-Developers.pdf
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diff --git a/langsmith/test-evaluation-book/main.py b/langsmith/test-evaluation-book/main.py
index f3498ae..a047d28 100644
--- a/langsmith/test-evaluation-book/main.py
+++ b/langsmith/test-evaluation-book/main.py
@@ -98,6 +98,7 @@ def ask_question_multiquery(question: str):
     # Chain
     qa_chain = LLMChain(llm=llm, prompt=QA_PROMPT)
 
+    # LEGACY CODE - REPLACED BY LCEL
     retrieval_chain = TransformChain(
         input_variables=["question"],
         output_variables=["query", "contexts"],
@@ -128,4 +129,4 @@ def ask_question_multiquery(question: str):
     # ask_question(question=question)
 
     ## ASK QUESTION WITH MULTIQUERY RETRIEVER
-    # ask_question_multiquery(question=question)
+    ask_question_multiquery(question=question)
diff --git a/lcel/how_it_works.py b/lcel/how_it_works.py
new file mode 100644
index 0000000..54a6dac
--- /dev/null
+++ b/lcel/how_it_works.py
@@ -0,0 +1,114 @@
+from types import UnionType
+from typing import Any
+from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+import os
+
+# embedding
+embedding = AzureOpenAIEmbeddings(
+    deployment=os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
+    chunk_size=1,
+    embedding_ctx_length=1000,
+)
+
+# llm
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+
+# This is demostrate how pipe operator works behind the scene
+class Runnable:
+    def __init__(self, func):
+        self.func = func
+
+    def __or__(self, other):
+        def chained_func(*args, **kwargs):
+            return other(self.func(*args, **kwargs))
+
+        return Runnable(chained_func)
+
+    def __call__(self, *args, **kwargs):
+        return self.func(*args, **kwargs)
+
+
+def add_one(x):
+    return x + 1
+
+
+def multiply_by_two(x):
+    return x * 2
+
+
+def test_simple_use():
+    add_one_chain = Runnable(add_one)
+    multiply_by_two_chain = Runnable(multiply_by_two)
+
+    # using the method
+    # chain = add_one_chain.__or__(multiply_by_two_chain)
+    # print(chain(5))
+
+    # using the operator
+    chain = add_one_chain | multiply_by_two_chain
+    print(chain(5))
+
+
+def run_in_parallel_simple_example():
+    from langchain_core.runnables import (
+        RunnableParallel,
+        RunnablePassthrough,
+        RunnableLambda,
+    )
+    from langchain.vectorstores.docarray import DocArrayInMemorySearch
+    from langchain.prompts import ChatPromptTemplate
+    from langchain.schema.output_parser import StrOutputParser
+
+    vecstore_a = DocArrayInMemorySearch.from_texts(
+        ["Tom is a senior software dev", "Tom was born XXX"], embedding=embedding
+    )
+    vecstore_b = DocArrayInMemorySearch.from_texts(
+        ["Sam was born in 1922", "Sam is a musician"], embedding=embedding
+    )
+
+    retriever_a = vecstore_a.as_retriever()
+    retriever_b = vecstore_b.as_retriever()
+
+    prompt_str = """Answer the question below using the context:
+    
+    Context: 
+    {context_a}
+    {context_b}
+
+    Question: {question}    
+    
+    Answer: """
+
+    prompt = ChatPromptTemplate.from_template(prompt_str)
+
+    retrieval = RunnableParallel(
+        {
+            "context_a": retriever_a,
+            "context_b": retriever_b,
+            "question": RunnablePassthrough(),
+        }
+    )
+
+    # demostrate runnable lambda with custom function
+    def return_when_point(x):
+        if "." in x:
+            return "\n".join(x.split(".")[:-1]) + "."
+        else:
+            return x
+
+    # Chain
+    chain = (
+        retrieval | prompt | llm | StrOutputParser() | RunnableLambda(return_when_point)
+    )
+
+    out = chain.invoke("Question")
+    print(out)
+
+
+run_in_parallel_simple_example()
diff --git a/lcel/multi-chain-py b/lcel/multi-chain-py
new file mode 100644
index 0000000..a00398a
--- /dev/null
+++ b/lcel/multi-chain-py
@@ -0,0 +1,33 @@
+from operator import itemgetter
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
+
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema import StrOutputParser
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+prompt1 = ChatPromptTemplate.from_template("Who is the singer of {band}?")
+prompt2 = ChatPromptTemplate.from_template(
+    "Where {singer} was born? Remember to speak as {style} when giving your final answer."
+)
+
+# llm
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+
+chain1 = prompt1 | llm | StrOutputParser()
+
+chain2 = (
+    {"singer": chain1, "style": itemgetter("style")} | prompt2 | llm | StrOutputParser()
+)
+
+response = chain2.invoke({"band": "Iron Maiden", "style": "hip hop rapper"})
+print(response)
diff --git a/lcel/rag-sample.py b/lcel/rag-sample.py
new file mode 100644
index 0000000..c568abe
--- /dev/null
+++ b/lcel/rag-sample.py
@@ -0,0 +1,95 @@
+from operator import itemgetter
+
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+from langchain.vectorstores import FAISS
+from langchain.vectorstores.chroma import Chroma
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# embedding
+embedding = AzureOpenAIEmbeddings(
+    deployment=os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
+    chunk_size=1,
+    embedding_ctx_length=1000,
+)
+
+# llm
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+
+def _get_retriever():
+    vectorstore = Chroma.from_texts(
+        ["Luca is passionate about LLMs and GenerativeAi"], embedding=embedding
+    )
+    retriever = vectorstore.as_retriever()
+    return retriever
+
+
+def simple_rag(question: str):
+    retriever = _get_retriever()
+
+    template = """Answer the question based only on the following context:
+    {context}
+
+    Question: {question}
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    result = chain.invoke(question)
+    return result
+
+
+def rag_with_different_inputs(question: str, style: str):
+    retriever = _get_retriever()
+
+    template = """Answer the question based only on the following context:
+    {context}
+
+    Question: {question}
+
+    Remember to speak as {style} when giving your final answer.
+    Use emojis if you want.
+    """
+
+    prompt = ChatPromptTemplate.from_template(template)
+
+    chain = (
+        {
+            "context": itemgetter("question") | retriever,
+            "question": itemgetter("question"),
+            "style": itemgetter("style"),
+        }
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    result = chain.invoke({"question": question, "style": style})
+    return result
+
+
+if __name__ == "__main__":
+    question = "What is Luca passionate about?"
+
+    # result = simple_rag(question=question)
+    style = "Heavy Metal Fan"
+    result = rag_with_different_inputs(question=question, style=style)
+    print(result)
diff --git a/lcel/rag-with-docs.py b/lcel/rag-with-docs.py
new file mode 100644
index 0000000..c09467e
--- /dev/null
+++ b/lcel/rag-with-docs.py
@@ -0,0 +1,166 @@
+from operator import itemgetter
+
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.vectorstores.chroma import Chroma
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+persist_directory = "data/new_chroma"
+collection_name = "rag_with_lcel"
+
+# embedding
+embedding = AzureOpenAIEmbeddings(
+    deployment=os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
+    chunk_size=1,
+    embedding_ctx_length=1000,
+)
+
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+
+def load_from_texts():
+    # vector db
+    vectorstore = Chroma.from_texts(
+        ["harrison worked at kensho"],
+        persist_directory=persist_directory,
+        embedding=embedding,
+        collection_name=collection_name,
+    )
+
+    return vectorstore
+
+
+def get_retriever():
+    vector_db = Chroma(
+        persist_directory=persist_directory,
+        embedding_function=embedding,
+        collection_name=collection_name,
+    )
+    retriever = vector_db.as_retriever()
+
+    return retriever
+
+
+# functions
+def ask_v1(question: str):
+    template = """Answer the question based only on the following context:
+    {context}
+
+    Question: {question}
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    chain = (
+        {"context": get_retriever(), "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    chain.invoke(question)
+
+
+def ask_v2(question: str, language: str):
+    template = """Answer the question based only on the following context:
+    {context}
+
+    Question: {question}
+
+    Answer in the following language: {language}
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    chain = (
+        {
+            "context": itemgetter("question") | get_retriever(),
+            "question": itemgetter("question"),
+            "language": itemgetter("language"),
+        }
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    result = chain.invoke({"question": question, "language": language})
+    return result
+
+
+def ask_v3(question: str, language: str):
+    """Conversational retrieval chain"""
+    from langchain.prompts.prompt import PromptTemplate
+    from langchain.schema import format_document
+    from langchain_core.runnables import RunnableParallel
+    from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string
+
+    # template for question condensation
+    _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+
+    Chat History:
+    {chat_history}
+    Follow Up Input: {question}
+    Standalone question:"""
+
+    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+    # template for answer
+    template = """Answer the question based only on the following context:
+    {context}
+
+    Question: {question}
+    """
+    ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
+
+    DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
+
+    def _combine_documents(
+        docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
+    ):
+        doc_strings = [format_document(doc, document_prompt) for doc in docs]
+        return document_separator.join(doc_strings)
+
+    _inputs = RunnableParallel(
+        standalone_question=RunnablePassthrough.assign(
+            chat_history=lambda x: get_buffer_string(x["chat_history"])
+        )
+        | CONDENSE_QUESTION_PROMPT
+        | llm
+        | StrOutputParser(),
+    )
+
+    _context = {
+        "context": itemgetter("standalone_question")
+        | get_retriever()
+        | _combine_documents,
+        "question": lambda x: x["standalone_question"],
+    }
+
+    conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | llm
+
+    conversational_qa_chain.invoke(
+        {
+            "question": "where did harrison work?",
+            "chat_history": [],
+        }
+    )
+
+
+# Main
+# load data
+
+# load_from_texts()
+
+# result = ask_v1("where did harrison work?")
+result = ask_v2("where did harrison work?", "italian")
+
+print(result)
diff --git a/lcel/simple-chain.py b/lcel/simple-chain.py
new file mode 100644
index 0000000..070c25b
--- /dev/null
+++ b/lcel/simple-chain.py
@@ -0,0 +1,54 @@
+from dotenv import load_dotenv
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+import os
+from langchain.schema.output_parser import StrOutputParser
+from langchain.pydantic_v1 import BaseModel, Field, validator
+from langchain.output_parsers import PydanticOutputParser
+from langchain.output_parsers.json import SimpleJsonOutputParser
+
+
+class Joke(BaseModel):
+    setup: str = Field(description="question to set up a joke")
+    punchline: str = Field(description="answer to resolve the joke")
+
+
+def get_simple_chain(query: str, parser: None):
+    # llm
+    model = AzureChatOpenAI(
+        azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+        openai_api_type="azure",
+        temperature=0.0,
+    )
+
+    prompt = ChatPromptTemplate.from_template(query)
+
+    # simple chain without parser
+    chain = prompt | model
+
+    # add parser if provided
+    if parser is not None:
+        chain = prompt | model | parser
+
+    return chain
+
+
+if __name__ == "__main__":
+    query = "tell me a joke about {topic}."
+
+    # simple
+    chain = get_simple_chain(query=query, parser=StrOutputParser())
+    response = chain.invoke({"topic": "bears"})
+    print(response)
+
+    # stream
+    # chain = get_simple_chain(query=query, parser=None)
+    # for s in chain.stream({"topic": "programming"}):
+    #     print(s.content, end="", flush=True)
+
+    # batch
+    # chain = get_simple_chain(query=query, parser=StrOutputParser())
+    # response = chain.batch(
+    #     [{"topic": "bears"}, {"topic": "cats"}], config={"max_concurrency": 5}
+    # )
+    # print(response)
diff --git a/rag/rag-lama.py b/rag/rag-lama.py
new file mode 100644
index 0000000..b333e59
--- /dev/null
+++ b/rag/rag-lama.py
@@ -0,0 +1,146 @@
+import logging
+import sys
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index import (
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    load_index_from_storage,
+    StorageContext,
+    ServiceContext,
+    Document,
+)
+
+from llama_index.llms import AzureOpenAI
+from llama_index.node_parser import (
+    SentenceWindowNodeParser,
+    HierarchicalNodeParser,
+    get_leaf_nodes,
+)
+from llama_index.text_splitter import SentenceSplitter
+from llama_index.embeddings import AzureOpenAIEmbedding, HuggingFaceEmbedding
+from llama_index.schema import MetadataMode
+from llama_index.postprocessor import MetadataReplacementPostProcessor
+from llama_index import set_global_service_context
+from llama_index.llms.types import ChatMessage
+import chromadb
+from llama_index.vector_stores import ChromaVectorStore
+
+file_content = ""
+with open("docs/detrazioni-miste.txt", "r") as file:
+    file_content = file.read()
+
+documents = [Document(text=file_content)]
+
+
+api_key = os.getenv("OPENAI_API_KEY")
+azure_endpoint = os.getenv("OPENAI_API_BASE")
+api_version = os.getenv("OPENAI_API_VERSION")
+
+llm = AzureOpenAI(
+    engine="chat",
+    temperature=0,
+    api_key=api_key,
+    azure_endpoint=azure_endpoint,
+    api_version=api_version,
+)
+
+# messages = [
+#     ChatMessage(role="system", content="You are a pirate with colorful personality."),
+#     ChatMessage(role="user", content="Hello"),
+# ]
+
+# response = llm.chat(messages)
+# print(response)
+
+
+# You need to deploy your own embedding model as well as your own chat completion model
+embed_model = AzureOpenAIEmbedding(
+    azure_deployment="embeddings",
+    api_key=api_key,
+    azure_endpoint=azure_endpoint,
+    api_version=api_version,
+)
+
+
+# save to disk
+# db = chromadb.PersistentClient(path="./chroma_db")
+# chroma_collection = db.get_or_create_collection("detrazioni")
+# vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+# storage_context = StorageContext.from_defaults(vector_store=vector_store)
+service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
+
+# index = VectorStoreIndex.from_documents(
+#     documents, storage_context=storage_context, service_context=service_context
+# )
+
+
+# load from disk
+db2 = chromadb.PersistentClient(path="./chroma_db")
+chroma_collection = db2.get_or_create_collection("detrazioni")
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+index = VectorStoreIndex.from_vector_store(
+    vector_store,
+    service_context=service_context,
+)
+
+
+from llama_index.llms import ChatMessage, MessageRole
+from llama_index.prompts import ChatPromptTemplate
+
+# Text QA Prompt
+chat_text_qa_msgs = [
+    ChatMessage(
+        role=MessageRole.SYSTEM,
+        content=("You are an helpful chat assistant. You are here to help the user."),
+    ),
+    ChatMessage(
+        role=MessageRole.USER,
+        content=(
+            "Context information is below.\n"
+            "---------------------\n"
+            "{context_str}\n"
+            "---------------------\n"
+            "Given the context information and not prior knowledge, "
+            "answer the question: {query_str}\n"
+        ),
+    ),
+]
+text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)
+
+# Refine Prompt
+chat_refine_msgs = [
+    ChatMessage(
+        role=MessageRole.SYSTEM,
+        content=("Always answer the question, even if the context isn't helpful."),
+    ),
+    ChatMessage(
+        role=MessageRole.USER,
+        content=(
+            "We have the opportunity to refine the original answer "
+            "(only if needed) with some more context below.\n"
+            "------------\n"
+            "{context_msg}\n"
+            "------------\n"
+            "Given the new context, refine the original answer to better "
+            "answer the question: {query_str}. "
+            "If the context isn't useful, output the original answer again.\n"
+            "Original Answer: {existing_answer}"
+        ),
+    ),
+]
+refine_template = ChatPromptTemplate(chat_refine_msgs)
+
+
+query_engine = index.as_query_engine(
+    text_qa_template=text_qa_template, refine_template=refine_template
+)
+response = query_engine.query("Question")
+
+print(response)
diff --git a/rag/rag-llama-parent-child.py b/rag/rag-llama-parent-child.py
new file mode 100644
index 0000000..ed9eff7
--- /dev/null
+++ b/rag/rag-llama-parent-child.py
@@ -0,0 +1,2 @@
+# IMPLEMENT THIS
+# https://ai.gopubby.com/advanced-retrieval-techniques-in-rag-part-02-parent-document-retrieval-334cd924a36e
diff --git a/rag/rag-llama-window-retrieval.py b/rag/rag-llama-window-retrieval.py
new file mode 100644
index 0000000..4784da0
--- /dev/null
+++ b/rag/rag-llama-window-retrieval.py
@@ -0,0 +1,282 @@
+# https://www.youtube.com/watch?v=oDzWsynpOyI
+
+
+import logging
+import sys
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+from llama_index import (
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    load_index_from_storage,
+    StorageContext,
+    ServiceContext,
+    Document,
+)
+
+import json
+import llama_index
+from llama_index.llms import AzureOpenAI
+from llama_index.node_parser import (
+    SentenceWindowNodeParser,
+    HierarchicalNodeParser,
+    get_leaf_nodes,
+)
+from llama_index.text_splitter import SentenceSplitter
+from llama_index.embeddings import AzureOpenAIEmbedding, HuggingFaceEmbedding
+from llama_index.schema import MetadataMode
+from llama_index.postprocessor import (
+    MetadataReplacementPostProcessor,
+    SimilarityPostprocessor,
+)
+from llama_index import set_global_service_context
+from llama_index.llms.types import ChatMessage
+import chromadb
+from llama_index.vector_stores import ChromaVectorStore
+
+
+### THE LLM
+api_key = os.getenv("OPENAI_API_KEY")
+azure_endpoint = os.getenv("OPENAI_API_BASE")
+api_version = os.getenv("OPENAI_API_VERSION")
+
+llm = AzureOpenAI(
+    engine="chat",
+    temperature=0.1,
+    api_key=api_key,
+    azure_endpoint=azure_endpoint,
+    api_version=api_version,
+)
+
+
+embed_model = AzureOpenAIEmbedding(
+    azure_deployment="embeddings",
+    api_key=api_key,
+    azure_endpoint=azure_endpoint,
+    api_version=api_version,
+)
+
+
+def _print_docs(docs):
+    # inspect documents
+    print("length of documents: ", str(len(docs)))
+    print("-----")
+    print(docs)
+
+    print("-----Metadata-----")
+    for doc in docs:
+        print(doc.metadata)
+
+
+def _print_nodes(name, nodes):
+    print("-----" + name + "-----")
+    counter = 1
+    for node in nodes:
+        print(f"-----Node {counter}")
+        dict_node = dict(node)
+
+        print(dict_node)
+        counter += 1
+
+    print("-----")
+
+
+def _create_text_qa_template():
+    from llama_index.llms import ChatMessage, MessageRole
+
+    from llama_index.prompts import ChatPromptTemplate
+
+    # Text QA Prompt
+    chat_text_qa_msgs = [
+        ChatMessage(
+            role=MessageRole.SYSTEM,
+            content=(
+                "You are an helpful chat assistant. You are here to help the user.Answer must be in the original language."
+            ),
+        ),
+        ChatMessage(
+            role=MessageRole.USER,
+            content=(
+                "Context information is below.\n"
+                "---------------------\n"
+                "{context_str}\n"
+                "---------------------\n"
+                "Given the context information and not prior knowledge,"
+                "answer the question: {query_str}\n"
+            ),
+        ),
+    ]
+    text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)
+
+    return text_qa_template
+
+
+def _create_refine_template():
+    from llama_index.llms import ChatMessage, MessageRole
+
+    from llama_index.prompts import ChatPromptTemplate
+
+    # Refine Prompt
+    chat_refine_msgs = [
+        ChatMessage(
+            role=MessageRole.SYSTEM,
+            content=("Always answer the question, even if the context isn't helpful."),
+        ),
+        ChatMessage(
+            role=MessageRole.USER,
+            content=(
+                "We have the opportunity to refine the original answer "
+                "(only if needed) with some more context below.\n"
+                "------------\n"
+                "{context_msg}\n"
+                "------------\n"
+                "Given the new context, refine the original answer to better "
+                "answer the question: {query_str}. "
+                "If the context isn't useful, output the original answer again.\n"
+                "Original Answer: {existing_answer}"
+            ),
+        ),
+    ]
+
+    refine_template = ChatPromptTemplate(chat_refine_msgs)
+    return refine_template
+
+
+def create_window_nodes(path="./sample-docs/"):
+    # get the file
+    documents = SimpleDirectoryReader(path).load_data()
+    # _print_docs(documents)
+
+    sentence_node_parser = SentenceWindowNodeParser.from_defaults(
+        window_size=3,
+        window_metadata_key="window",
+        original_text_metadata_key="original_text",
+    )
+    window_nodes = sentence_node_parser.get_nodes_from_documents(documents)
+
+    # _print_nodes("WINDOW NODES", window_nodes)
+    return window_nodes
+
+
+def create_base_nodes(path="./sample-docs/"):
+    # get the file
+    documents = SimpleDirectoryReader(path).load_data()
+    # _print_docs(documents)
+
+    base_node_parser = SentenceSplitter()
+
+    base_nodes = base_node_parser.get_nodes_from_documents(documents)
+
+    # _print_nodes("BASE NODES", base_nodes)
+    return base_nodes
+
+
+def save_on_chroma_and_get_index(nodes, collection_name):
+    ### CREATE THE VECTOR STORES
+    ### SAVING VECTORS ON DISK
+    db = chromadb.PersistentClient(path="./chroma_db")
+
+    vector_collection = db.get_or_create_collection(collection_name)
+    vector_store = ChromaVectorStore(chroma_collection=vector_collection)
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+    ctx = ServiceContext.from_defaults(
+        llm=llm, embed_model=embed_model, node_parser=nodes
+    )
+
+    index = VectorStoreIndex(
+        nodes, storage_context=storage_context, service_context=ctx
+    )
+
+    return index
+
+
+def get_index(collection_name):
+    db2 = chromadb.PersistentClient(path="./chroma_db")
+    service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
+
+    collection = db2.get_or_create_collection(collection_name)
+    vector_store = ChromaVectorStore(chroma_collection=collection)
+    index = VectorStoreIndex.from_vector_store(
+        vector_store,
+        service_context=service_context,
+    )
+
+    return index
+
+
+def run_window_index_sample(question):
+    window_index = get_index("window-detrazioni")
+    text_qa_template = _create_text_qa_template()
+    refine_template = _create_refine_template()
+
+    window_query_engine = window_index.as_query_engine(
+        similarity_top_k=5,
+        verbose=True,
+        text_qa_template=text_qa_template,
+        # refine_template=refine_template,
+        node_postprocessor=MetadataReplacementPostProcessor(
+            target_metadata_key="window",
+        )
+        # node_postprocessors=[
+        #     SimilarityPostprocessor(similarity_cutoff=0.7),
+        #     MetadataReplacementPostProcessor(
+        #         target_metadata_key="window",
+        #     ),
+        # ],
+    )
+
+    base_response = window_query_engine.query(question)
+    print(base_response)
+
+
+def run_base_index_sample(question):
+    base_index = get_index("base-detrazioni")
+    text_qa_template = _create_text_qa_template()
+    refine_template = _create_refine_template()
+
+    # Query engine
+    # base_query_engine = base_index.as_query_engine(
+    #     verbose=True,
+    #     text_qa_template=text_qa_template,
+    #     # refine_template=refine_template,
+    # )
+
+    # chat engine
+
+    base_query_engine = base_index.as_chat_engine()
+
+    base_response = base_query_engine.chat(question)
+    print(base_response)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+    logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+    # windows_node = create_window_nodes()
+    # window_index = save_on_chroma_and_get_index(windows_node, "window-detrazioni")
+
+    ### INFERENCE
+    question = "question!!!"
+    # window_index = run_window_index_sample(question=question)
+    base_index = run_base_index_sample(question=question)
+
+# ### TODO : TO INVESTIGATE
+# ### SAVING INDEX DEFINITION ON DISK
+# ### this is useful to avoid having to recreate the index every time so we can save money
+# ### from embedding calls
+# window_index.storage_context.persist(persist_dir="./window-indexes")
+# base_index.storage_context.persist(persist_dir="./base-indexes")
+
+# ### RELOAD INDEXES FROM DISK
+# SC_retrieved_window = storage_context_window.from_defaults(
+#     persist_dir="./window-indexes"
+# )
+# SC_retrieved_base = storage_context_base.from_defaults(persist_dir="./base-indexes")
+
+# retrieved_window_index = load_index_from_storage(SC_retrieved_window)
+# retrieved_base_index = load_index_from_storage(SC_retrieved_base)
diff --git a/rag/rag.py b/rag/rag.py
new file mode 100644
index 0000000..03f61ec
--- /dev/null
+++ b/rag/rag.py
@@ -0,0 +1,169 @@
+# Simple rag with simple "approach" doesn't work well when the context
+# of document referring to the same topic for different business context
+# Example : Searching for "XXXX" in a document where "XXXX"is mentioned
+# for different scenario
+
+import os
+from operator import itemgetter
+from typing import List, Tuple
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema import AIMessage, HumanMessage, format_document
+from langchain.vectorstores import Pinecone
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.runnables import (
+    RunnableBranch,
+    RunnableLambda,
+    RunnableParallel,
+    RunnablePassthrough,
+)
+from langchain.vectorstores.azuresearch import AzureSearch
+from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
+from langchain.chat_models.azure_openai import AzureChatOpenAI
+
+embedding = AzureOpenAIEmbeddings(
+    deployment=os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
+    chunk_size=1,
+    embedding_ctx_length=1000,
+)
+
+llm = AzureChatOpenAI(
+    azure_deployment=os.getenv("OPENAI_CHAT_DEPLOYMENT_NAME"),
+    openai_api_type="azure",
+    temperature=0.0,
+)
+
+index_name = "cdr"
+vector_store = AzureSearch(
+    azure_search_endpoint=os.getenv("AZURE_SEARCH_SERVICE"),
+    azure_search_key=os.getenv("AZURE_SEARCH_KEY"),
+    index_name=index_name,
+    embedding_function=embedding.embed_query,
+)
+
+# Condense a chat history and follow-up question into a standalone question
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""  # noqa: E501
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+# RAG answer synthesis prompt
+template = """Answer the question based only on the following context:
+<context>
+{context}
+</context>"""
+ANSWER_PROMPT = ChatPromptTemplate.from_messages(
+    [
+        ("system", template),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{question}"),
+    ]
+)
+
+# Conversational Retrieval Chain
+DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
+
+
+def _combine_documents(
+    docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
+):
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
+
+
+def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List:
+    buffer = []
+    for human, ai in chat_history:
+        buffer.append(HumanMessage(content=human))
+        buffer.append(AIMessage(content=ai))
+    return buffer
+
+
+def _get_docs_metadata(docs) -> List:
+    metadata = []
+    for doc in docs:
+        metadata.append({"id": doc["metadata"].page})
+
+    return metadata
+
+
+# User input
+class ChatHistory(BaseModel):
+    chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
+    question: str
+
+
+_search_query = RunnableBranch(
+    # If input includes chat_history, we condense it with the follow-up question
+    (
+        RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
+            run_name="HasChatHistoryCheck"
+        ),  # Condense follow-up question and chat into a standalone_question
+        RunnablePassthrough.assign(
+            chat_history=lambda x: _format_chat_history(x["chat_history"])
+        )
+        | CONDENSE_QUESTION_PROMPT
+        | llm
+        | StrOutputParser(),
+    ),
+    # Else, we have no chat history, so just pass through the question
+    RunnableLambda(itemgetter("question")),
+)
+
+#
+retriever = vector_store.as_retriever(
+    search_type="similarity",
+    search_kwargs={"k": 3, "fetch_k": 20, "score_threshold": 0.90},
+)
+
+_inputs = RunnableParallel(
+    {
+        "question": lambda x: x["question"],
+        "chat_history": lambda x: _format_chat_history(x["chat_history"]),
+        "context": _search_query | retriever | _combine_documents,
+    }
+).with_types(input_type=ChatHistory)
+
+chain = _inputs | ANSWER_PROMPT | llm | StrOutputParser()
+
+
+result = chain.invoke(
+    {
+        "question": "Chi fa parte del gruppo di lavoro bilanci dello studio?",
+        "chat_history": [],
+    }
+)
+
+
+print(result)
+
+
+#####  LANGSERVE  #####
+#!/usr/bin/env python
+# from fastapi import FastAPI
+# from langserve import add_routes
+
+
+# app = FastAPI(
+#     title="LangChain Server",
+#     version="1.0",
+#     description="A simple api server using Langchain's Runnable interfaces",
+# )
+
+# add_routes(
+#     app,
+#     chain,
+#     path="/rag",
+# )
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)
diff --git a/requirements.txt b/requirements.txt
index 43b0a08..0b1e557 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,93 +1,129 @@
-aiohttp==3.9.0; python_version >= '3.8'
+-i https://pypi.org/simple
+aiohttp==3.9.1; python_version >= '3.8'
 aiosignal==1.3.1; python_version >= '3.7'
-annotated-types==0.6.0; python_version >= '3.8'
 anyio==3.7.1; python_version >= '3.7'
+asgiref==3.7.2; python_version >= '3.7'
 attrs==23.1.0; python_version >= '3.7'
+azure-common==1.1.28
+azure-core==1.29.6; python_version >= '3.7'
+azure-identity==1.15.0
+azure-search-documents==11.4.0b8
 backoff==2.2.1; python_version >= '3.7'
-bcrypt==4.0.1; python_version >= '3.6'
+bcrypt==4.1.2; python_version >= '3.7'
+beautifulsoup4==4.12.2
 cachetools==5.3.2; python_version >= '3.7'
 certifi==2023.11.17; python_version >= '3.6'
+cffi==1.16.0; python_version >= '3.8'
 charset-normalizer==3.3.2; python_full_version >= '3.7.0'
 chroma-hnswlib==0.7.3
-chromadb==0.4.17
+chromadb==0.4.20
 click==8.1.7; python_version >= '3.7'
 colorama==0.4.6; platform_system == 'Windows'
 coloredlogs==15.0.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
-dataclasses-json==0.6.2; python_version >= '3.7' and python_version < '4.0'
+cryptography==41.0.7; python_version >= '3.7'
+curl-cffi==0.5.10; python_version >= '3.7'
+dataclasses-json==0.6.3; python_version >= '3.7' and python_version < '4.0'
 deprecated==1.2.14; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
 distro==1.8.0; python_version >= '3.6'
-fastapi==0.104.1; python_version >= '3.8'
+docarray==0.39.1
+duckduckgo-search==4.1.0
+fastapi==0.105.0; python_version >= '3.8'
 filelock==3.13.1; python_version >= '3.8'
 flatbuffers==23.5.26
-frozenlist==1.4.0; python_version >= '3.8'
-fsspec==2023.10.0; python_version >= '3.8'
-google-auth==2.23.4; python_version >= '3.7'
-googleapis-common-protos==1.61.0; python_version >= '3.7'
-greenlet==3.0.1; platform_machine == 'aarch64' or (platform_machine == 'ppc64le' or (platform_machine == 'x86_64' or (platform_machine == 'amd64' or (platform_machine == 'AMD64' or (platform_machine == 'win32' or platform_machine == 'WIN32')))))
-grpcio==1.59.3; python_version >= '3.7'
+frozenlist==1.4.1; python_version >= '3.8'
+fsspec==2023.12.2; python_version >= '3.8'
+google-auth==2.25.2; python_version >= '3.7'
+googleapis-common-protos==1.62.0; python_version >= '3.7'
+greenlet==3.0.2; platform_machine == 'aarch64' or (platform_machine == 'ppc64le' or (platform_machine == 'x86_64' or (platform_machine == 'amd64' or (platform_machine == 'AMD64' or (platform_machine == 'win32' or platform_machine == 'WIN32')))))
+grpcio==1.60.0; python_version >= '3.7'
 h11==0.14.0; python_version >= '3.7'
 httpcore==1.0.2; python_version >= '3.8'
 httptools==0.6.1
-httpx==0.25.1; python_version >= '3.8'
+httpx==0.25.2; python_version >= '3.8'
 huggingface-hub==0.19.4; python_full_version >= '3.8.0'
 humanfriendly==10.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
-idna==3.4; python_version >= '3.5'
-importlib-metadata==6.8.0; python_version >= '3.8'
+idna==3.6; python_version >= '3.5'
+importlib-metadata==6.11.0; python_version >= '3.8'
 importlib-resources==6.1.1; python_version >= '3.8'
+isodate==0.6.1
 jsonpatch==1.33; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6'
 jsonpointer==2.4; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6'
 kubernetes==28.1.0; python_version >= '3.6'
-langchain[]==0.0.337
-langsmith==0.0.65
+langchain==0.0.351
+langchain-community==0.0.4; python_version < '4.0' and python_full_version >= '3.8.1'
+langchain-core==0.1.1; python_version < '4.0' and python_full_version >= '3.8.1'
+langserve==0.0.36
+langsmith==0.0.72
+lxml==4.9.3; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'
+markdown-it-py==3.0.0; python_version >= '3.8'
 marshmallow==3.20.1; python_version >= '3.8'
+mdurl==0.1.2; python_version >= '3.7'
+mmh3==4.0.1
 monotonic==1.6
 mpmath==1.3.0
+msal==1.26.0; python_version >= '2.7'
+msal-extensions==1.1.0; python_version >= '3.7'
 multidict==6.0.4; python_version >= '3.7'
 mypy-extensions==1.0.0; python_version >= '3.5'
-numpy==1.26.2; python_version >= '3.8'
+numpy==1.26.2; python_version >= '3.9'
 oauthlib==3.2.2; python_version >= '3.6'
-onnxruntime==1.16.2
-openai==1.3.3
+onnxruntime==1.16.3
+openai==1.5.0
 opentelemetry-api==1.21.0; python_version >= '3.7'
 opentelemetry-exporter-otlp-proto-common==1.21.0; python_version >= '3.7'
 opentelemetry-exporter-otlp-proto-grpc==1.21.0; python_version >= '3.7'
+opentelemetry-instrumentation==0.42b0; python_version >= '3.7'
+opentelemetry-instrumentation-asgi==0.42b0; python_version >= '3.7'
+opentelemetry-instrumentation-fastapi==0.42b0; python_version >= '3.7'
 opentelemetry-proto==1.21.0; python_version >= '3.7'
 opentelemetry-sdk==1.21.0; python_version >= '3.7'
 opentelemetry-semantic-conventions==0.42b0; python_version >= '3.7'
+opentelemetry-util-http==0.42b0; python_version >= '3.7'
+orjson==3.9.10; python_version >= '3.8'
 overrides==7.4.0; python_version >= '3.6'
 packaging==23.2; python_version >= '3.7'
-posthog==3.0.2
+portalocker==2.8.2; platform_system == 'Windows'
+posthog==3.1.0
 protobuf==4.25.1; python_version >= '3.8'
 pulsar-client==3.3.0
-pyasn1==0.5.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
+pyasn1==0.5.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
 pyasn1-modules==0.3.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
-pydantic==2.5.1; python_version >= '3.7'
-pydantic-core==2.14.3; python_version >= '3.7'
-pypdf==3.17.1
+pycparser==2.21
+pydantic==1.10.13
+pygments==2.17.2; python_version >= '3.7'
+pyjwt[crypto]==2.8.0; python_version >= '3.7'
+pypdf==3.17.3
 pypika==0.48.9
 pyreadline3==3.4.1; python_version >= '3.8' and sys_platform == 'win32'
 python-dateutil==2.8.2; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
 python-dotenv==1.0.0
+pywin32==306; platform_system == 'Windows'
 pyyaml==6.0.1; python_version >= '3.6'
-requests==2.31.0; python_version >= '3.7'
+requests==2.31.0
 requests-oauthlib==1.3.1; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
+rich==13.7.0; python_full_version >= '3.7.0'
 rsa==4.9; python_version >= '3.6' and python_version < '4'
+setuptools==69.0.2; python_version >= '3.8'
 six==1.16.0; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'
 sniffio==1.3.0; python_version >= '3.7'
+soupsieve==2.5; python_version >= '3.8'
 sqlalchemy==2.0.23; python_version >= '3.7'
+sse-starlette==1.8.2
 starlette==0.27.0; python_version >= '3.7'
 sympy==1.12; python_version >= '3.8'
 tenacity==8.2.3; python_version >= '3.7'
 tokenizers==0.15.0; python_version >= '3.7'
 tqdm==4.66.1; python_version >= '3.7'
 typer==0.9.0; python_version >= '3.6'
-typing-extensions==4.8.0; python_version >= '3.8'
+types-requests==2.31.0.6; python_version >= '3.7'
+types-urllib3==1.26.25.14
+typing-extensions==4.9.0; python_version >= '3.8'
 typing-inspect==0.9.0
 urllib3==1.26.18; python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'
 uvicorn[standard]==0.24.0.post1; python_version >= '3.8'
 watchfiles==0.21.0
-websocket-client==1.6.4; python_version >= '3.8'
+websocket-client==1.7.0; python_version >= '3.8'
 websockets==12.0
 wrapt==1.16.0; python_version >= '3.6'
-yarl==1.9.2; python_version >= '3.7'
+yarl==1.9.4; python_version >= '3.7'
 zipp==3.17.0; python_version >= '3.8'