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Bagatur
2023-11-14 09:29:43 -08:00
parent 2f6fe6ddf3
commit f6e4c062f6
13 changed files with 220 additions and 176 deletions
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201
cookbook/advanced_rag_eval.ipynb
View File

@@ -63,11 +63,13 @@
"\n",
"# Load\n",
"from langchain.document_loaders import PyPDFLoader\n",
"\n",
"loader = PyPDFLoader(path + \"cpi.pdf\")\n",
"pdf_pages = loader.load()\n",
"\n",
"# Split\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
"all_splits_pypdf = text_splitter.split_documents(pdf_pages)\n",
"all_splits_pypdf_texts = [d.page_content for d in all_splits_pypdf]"
@@ -132,10 +134,13 @@
"source": [
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"baseline = Chroma.from_texts(texts=all_splits_pypdf_texts,\n",
" collection_name=\"baseline\",\n",
" embedding=OpenAIEmbeddings())\n",
"retriever_baseline=baseline.as_retriever()"
"\n",
"baseline = Chroma.from_texts(\n",
" texts=all_splits_pypdf_texts,\n",
" collection_name=\"baseline\",\n",
" embedding=OpenAIEmbeddings(),\n",
")\n",
"retriever_baseline = baseline.as_retriever()"
]
},
{
@@ -169,7 +174,7 @@
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()\n",
"\n",
"# Apply to text \n",
"# Apply to text\n",
"text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 5})\n",
"\n",
"# Apply to tables\n",
@@ -197,26 +202,25 @@
"from PIL import Image\n",
"from langchain.schema.messages import HumanMessage\n",
"\n",
"def encode_image(image_path):\n",
" ''' Getting the base64 string '''\n",
" with open(image_path, \"rb\") as image_file:\n",
" return base64.b64encode(image_file.read()).decode('utf-8') \n",
"\n",
"def image_summarize(img_base64,prompt):\n",
" ''' Image summary '''\n",
" chat = ChatOpenAI(model=\"gpt-4-vision-preview\",\n",
" max_tokens=1024)\n",
" \n",
"def encode_image(image_path):\n",
" \"\"\"Getting the base64 string\"\"\"\n",
" with open(image_path, \"rb\") as image_file:\n",
" return base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
"\n",
"\n",
"def image_summarize(img_base64, prompt):\n",
" \"\"\"Image summary\"\"\"\n",
" chat = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
"\n",
" msg = chat.invoke(\n",
" [\n",
" HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\":prompt},\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": f\"data:image/jpeg;base64,{img_base64}\"\n",
" },\n",
" \"image_url\": {\"url\": f\"data:image/jpeg;base64,{img_base64}\"},\n",
" },\n",
" ]\n",
" )\n",
@@ -224,6 +228,7 @@
" )\n",
" return msg.content\n",
"\n",
"\n",
"# Store base64 encoded images\n",
"img_base64_list = []\n",
"\n",
@@ -237,11 +242,11 @@
"\n",
"# Apply to images\n",
"for img_file in sorted(os.listdir(path)):\n",
" if img_file.endswith('.jpg'):\n",
" if img_file.endswith(\".jpg\"):\n",
" img_path = os.path.join(path, img_file)\n",
" base64_image = encode_image(img_path)\n",
" img_base64_list.append(base64_image)\n",
" image_summaries.append(image_summarize(base64_image,prompt))"
" image_summaries.append(image_summarize(base64_image, prompt))"
]
},
{
@@ -267,14 +272,10 @@
"from langchain.schema.document import Document\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"\n",
"def create_multi_vector_retriever(vectorstore, \n",
" text_summaries, \n",
" texts, \n",
" table_summaries, \n",
" tables, \n",
" image_summaries, \n",
" images):\n",
" \n",
"\n",
"def create_multi_vector_retriever(\n",
" vectorstore, text_summaries, texts, table_summaries, tables, image_summaries, images\n",
"):\n",
" # Initialize the storage layer\n",
" store = InMemoryStore()\n",
" id_key = \"doc_id\"\n",
@@ -309,18 +310,22 @@
"\n",
" return retriever\n",
"\n",
"\n",
"# The vectorstore to use to index the summaries\n",
"multi_vector_img = Chroma(collection_name=\"multi_vector_img\", \n",
" embedding_function=OpenAIEmbeddings())\n",
"multi_vector_img = Chroma(\n",
" collection_name=\"multi_vector_img\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"\n",
"# Create retriever\n",
"retriever_multi_vector_img = create_multi_vector_retriever(multi_vector_img,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries, \n",
" tables, \n",
" image_summaries, \n",
" img_base64_list)"
"retriever_multi_vector_img = create_multi_vector_retriever(\n",
" multi_vector_img,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries,\n",
" tables,\n",
" image_summaries,\n",
" img_base64_list,\n",
")"
]
},
{
@@ -330,10 +335,10 @@
"metadata": {},
"outputs": [],
"source": [
"# Testing on retrieval \n",
"query=\"What percentage of CPI is dedicated to Housing, and how does it compare to the combined percentage of Medical Care, Apparel, and Other Goods and Services?\"\n",
"suffix_for_images=\" Include any pie charts, graphs, or tables.\"\n",
"docs = retriever_multi_vector_img.get_relevant_documents(query+suffix_for_images)"
"# Testing on retrieval\n",
"query = \"What percentage of CPI is dedicated to Housing, and how does it compare to the combined percentage of Medical Care, Apparel, and Other Goods and Services?\"\n",
"suffix_for_images = \" Include any pie charts, graphs, or tables.\"\n",
"docs = retriever_multi_vector_img.get_relevant_documents(query + suffix_for_images)"
]
},
{
@@ -357,14 +362,16 @@
],
"source": [
"from IPython.display import display, HTML\n",
"def plt_img_base64(img_base64):\n",
"\n",
"\n",
"def plt_img_base64(img_base64):\n",
" # Create an HTML img tag with the base64 string as the source\n",
" image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
" \n",
"\n",
" # Display the image by rendering the HTML\n",
" display(HTML(image_html))\n",
"\n",
"\n",
"plt_img_base64(docs[1])"
]
},
@@ -386,17 +393,20 @@
"outputs": [],
"source": [
"# The vectorstore to use to index the summaries\n",
"multi_vector_text = Chroma(collection_name=\"multi_vector_text\", \n",
" embedding_function=OpenAIEmbeddings())\n",
"multi_vector_text = Chroma(\n",
" collection_name=\"multi_vector_text\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"\n",
"# Create retriever\n",
"retriever_multi_vector_img_summary = create_multi_vector_retriever(multi_vector_text,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries, \n",
" tables, \n",
" image_summaries, \n",
" image_summaries)"
"retriever_multi_vector_img_summary = create_multi_vector_retriever(\n",
" multi_vector_text,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries,\n",
" tables,\n",
" image_summaries,\n",
" image_summaries,\n",
")"
]
},
{
@@ -418,14 +428,17 @@
"\n",
"# Create chroma w/ multi-modal embeddings\n",
"multimodal_embd = Chroma(\n",
" collection_name=\"multimodal_embd\",\n",
" embedding_function=OpenCLIPEmbeddings()\n",
" collection_name=\"multimodal_embd\", embedding_function=OpenCLIPEmbeddings()\n",
")\n",
"\n",
"# Get image URIs\n",
"image_uris = sorted([os.path.join(path, image_name) \n",
" for image_name in os.listdir(path) \n",
" if image_name.endswith('.jpg')])\n",
"image_uris = sorted(\n",
" [\n",
" os.path.join(path, image_name)\n",
" for image_name in os.listdir(path)\n",
" if image_name.endswith(\".jpg\")\n",
" ]\n",
")\n",
"\n",
"# Add images and documents\n",
"if image_uris:\n",
@@ -435,7 +448,7 @@
"if tables:\n",
" multimodal_embd.add_texts(texts=tables)\n",
"\n",
"# Make retriever \n",
"# Make retriever\n",
"retriever_multimodal_embd = multimodal_embd.as_retriever()"
]
},
@@ -466,14 +479,14 @@
"\"\"\"\n",
"rag_prompt_text = ChatPromptTemplate.from_template(template)\n",
"\n",
"# Build \n",
"\n",
"# Build\n",
"def text_rag_chain(retriever):\n",
" \n",
" ''' RAG chain '''\n",
" \"\"\"RAG chain\"\"\"\n",
"\n",
" # LLM\n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
" \n",
"\n",
" # RAG pipeline\n",
" chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
@@ -500,13 +513,15 @@
"metadata": {},
"outputs": [],
"source": [
"import re \n",
"import re\n",
"from langchain.schema import Document\n",
"from langchain.schema.runnable import RunnableLambda\n",
"\n",
"\n",
"def looks_like_base64(sb):\n",
" \"\"\"Check if the string looks like base64.\"\"\"\n",
" return re.match('^[A-Za-z0-9+/]+[=]{0,2}$', sb) is not None\n",
" return re.match(\"^[A-Za-z0-9+/]+[=]{0,2}$\", sb) is not None\n",
"\n",
"\n",
"def is_image_data(b64data):\n",
" \"\"\"Check if the base64 data is an image by looking at the start of the data.\"\"\"\n",
@@ -514,7 +529,7 @@
" b\"\\xFF\\xD8\\xFF\": \"jpg\",\n",
" b\"\\x89\\x50\\x4E\\x47\\x0D\\x0A\\x1A\\x0A\": \"png\",\n",
" b\"\\x47\\x49\\x46\\x38\": \"gif\",\n",
" b\"\\x52\\x49\\x46\\x46\": \"webp\"\n",
" b\"\\x52\\x49\\x46\\x46\": \"webp\",\n",
" }\n",
" try:\n",
" header = base64.b64decode(b64data)[:8] # Decode and get the first 8 bytes\n",
@@ -525,6 +540,7 @@
" except Exception:\n",
" return False\n",
"\n",
"\n",
"def split_image_text_types(docs):\n",
" \"\"\"Split base64-encoded images and texts.\"\"\"\n",
" b64_images = []\n",
@@ -539,6 +555,7 @@
" texts.append(doc)\n",
" return {\"images\": b64_images, \"texts\": texts}\n",
"\n",
"\n",
"def img_prompt_func(data_dict):\n",
" # Joining the context texts into a single string\n",
" formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n",
@@ -550,7 +567,7 @@
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": f\"data:image/jpeg;base64,{data_dict['context']['images'][0]}\"\n",
" }\n",
" },\n",
" }\n",
" messages.append(image_message)\n",
"\n",
@@ -563,22 +580,24 @@
" f\"User-provided question / keywords: {data_dict['question']}\\n\\n\"\n",
" \"Text and / or tables:\\n\"\n",
" f\"{formatted_texts}\"\n",
" )\n",
" ),\n",
" }\n",
" messages.append(text_message)\n",
" return [HumanMessage(content=messages)]\n",
"\n",
"\n",
"def multi_modal_rag_chain(retriever):\n",
" ''' Multi-modal RAG chain '''\n",
" \"\"\"Multi-modal RAG chain\"\"\"\n",
"\n",
" # Multi-modal LLM\n",
" model = ChatOpenAI(temperature=0, \n",
" model=\"gpt-4-vision-preview\", \n",
" max_tokens=1024)\n",
" \n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
"\n",
" # RAG pipeline\n",
" chain = (\n",
" {\"context\": retriever | RunnableLambda(split_image_text_types), \"question\": RunnablePassthrough()}\n",
" {\n",
" \"context\": retriever | RunnableLambda(split_image_text_types),\n",
" \"question\": RunnablePassthrough(),\n",
" }\n",
" | RunnableLambda(img_prompt_func)\n",
" | model\n",
" | StrOutputParser()\n",
@@ -603,12 +622,12 @@
"outputs": [],
"source": [
"# RAG chains\n",
"chain_baseline=text_rag_chain(retriever_baseline)\n",
"chain_mv_text=text_rag_chain(retriever_multi_vector_img_summary)\n",
"chain_baseline = text_rag_chain(retriever_baseline)\n",
"chain_mv_text = text_rag_chain(retriever_multi_vector_img_summary)\n",
"\n",
"# Multi-modal RAG chains\n",
"chain_multimodal_mv_img=multi_modal_rag_chain(retriever_multi_vector_img)\n",
"chain_multimodal_embd=multi_modal_rag_chain(retriever_multimodal_embd)"
"chain_multimodal_mv_img = multi_modal_rag_chain(retriever_multi_vector_img)\n",
"chain_multimodal_embd = multi_modal_rag_chain(retriever_multimodal_embd)"
]
},
{
@@ -694,7 +713,8 @@
"source": [
"# Read\n",
"import pandas as pd\n",
"eval_set = pd.read_csv(path+'cpi_eval.csv')\n",
"\n",
"eval_set = pd.read_csv(path + \"cpi_eval.csv\")\n",
"eval_set.head(3)"
]
},
@@ -715,12 +735,12 @@
"# Populate dataset\n",
"for _, row in eval_set.iterrows():\n",
" # Get Q, A\n",
" q = row['Question']\n",
" a = row['Answer']\n",
" q = row[\"Question\"]\n",
" a = row[\"Answer\"]\n",
" # Use the values in your function\n",
" client.create_example(inputs={\"question\": q}, \n",
" outputs={\"answer\": a}, \n",
" dataset_id=dataset.id)"
" client.create_example(\n",
" inputs={\"question\": q}, outputs={\"answer\": a}, dataset_id=dataset.id\n",
" )"
]
},
{
@@ -764,17 +784,22 @@
" evaluators=[\"qa\"],\n",
")\n",
"\n",
"def run_eval(chain,run_name,dataset_name):\n",
"\n",
"def run_eval(chain, run_name, dataset_name):\n",
" _ = client.run_on_dataset(\n",
" dataset_name=dataset_name,\n",
" llm_or_chain_factory=lambda: (lambda x: x[\"question\"]+suffix_for_images) | chain,\n",
" llm_or_chain_factory=lambda: (lambda x: x[\"question\"] + suffix_for_images)\n",
" | chain,\n",
" evaluation=eval_config,\n",
" project_name=run_name,\n",
" )\n",
"\n",
"for chain, run in zip([chain_baseline, chain_mv_text, chain_multimodal_mv_img, chain_multimodal_embd], \n",
" [\"baseline\", \"mv_text\", \"mv_img\", \"mm_embd\"]):\n",
" run_eval(chain, dataset_name+\"-\"+run, dataset_name)"
"\n",
"for chain, run in zip(\n",
" [chain_baseline, chain_mv_text, chain_multimodal_mv_img, chain_multimodal_embd],\n",
" [\"baseline\", \"mv_text\", \"mv_img\", \"mm_embd\"],\n",
"):\n",
" run_eval(chain, dataset_name + \"-\" + run, dataset_name)"
]
}
],

68
cookbook/multi_modal_RAG_chroma.ipynb
View File

@@ -115,7 +115,7 @@
"metadata": {},
"outputs": [],
"source": [
"# Folder with pdf and extracted images \n",
"# Folder with pdf and extracted images\n",
"path = \"/Users/rlm/Desktop/photos/\""
]
},
@@ -128,9 +128,10 @@
"source": [
"# Extract images, tables, and chunk text\n",
"from unstructured.partition.pdf import partition_pdf\n",
"\n",
"raw_pdf_elements = partition_pdf(\n",
" filename=path + \"photos.pdf\",\n",
" extract_images_in_pdf=True, \n",
" extract_images_in_pdf=True,\n",
" infer_table_structure=True,\n",
" chunking_strategy=\"by_title\",\n",
" max_characters=4000,\n",
@@ -191,14 +192,17 @@
"\n",
"# Create chroma\n",
"vectorstore = Chroma(\n",
" collection_name=\"mm_rag_clip_photos\",\n",
" embedding_function=OpenCLIPEmbeddings()\n",
" collection_name=\"mm_rag_clip_photos\", embedding_function=OpenCLIPEmbeddings()\n",
")\n",
"\n",
"# Get image URIs with .jpg extension only\n",
"image_uris = sorted([os.path.join(path, image_name) \n",
" for image_name in os.listdir(path) \n",
" if image_name.endswith('.jpg')])\n",
"image_uris = sorted(\n",
" [\n",
" os.path.join(path, image_name)\n",
" for image_name in os.listdir(path)\n",
" if image_name.endswith(\".jpg\")\n",
" ]\n",
")\n",
"\n",
"# Add images\n",
"vectorstore.add_images(uris=image_uris)\n",
@@ -206,7 +210,7 @@
"# Add documents\n",
"vectorstore.add_texts(texts=texts)\n",
"\n",
"# Make retriever \n",
"# Make retriever\n",
"retriever = vectorstore.as_retriever()"
]
},
@@ -235,6 +239,7 @@
"from io import BytesIO\n",
"from PIL import Image\n",
"\n",
"\n",
"def resize_base64_image(base64_string, size=(128, 128)):\n",
" \"\"\"\n",
" Resize an image encoded as a Base64 string.\n",
@@ -258,30 +263,31 @@
" resized_img.save(buffered, format=img.format)\n",
"\n",
" # Encode the resized image to Base64\n",
" return base64.b64encode(buffered.getvalue()).decode('utf-8')\n",
" return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n",
"\n",
"\n",
"def is_base64(s):\n",
" ''' Check if a string is Base64 encoded '''\n",
" \"\"\"Check if a string is Base64 encoded\"\"\"\n",
" try:\n",
" return base64.b64encode(base64.b64decode(s)) == s.encode()\n",
" except Exception:\n",
" return False\n",
" \n",
"\n",
"\n",
"def split_image_text_types(docs):\n",
" ''' Split numpy array images and texts '''\n",
" \"\"\"Split numpy array images and texts\"\"\"\n",
" images = []\n",
" text = []\n",
" for doc in docs:\n",
" doc = doc.page_content # Extract Document contents \n",
" doc = doc.page_content # Extract Document contents\n",
" if is_base64(doc):\n",
" # Resize image to avoid OAI server error\n",
" images.append(resize_base64_image(doc, size=(250, 250))) # base64 encoded str \n",
" images.append(\n",
" resize_base64_image(doc, size=(250, 250))\n",
" ) # base64 encoded str\n",
" else:\n",
" text.append(doc) \n",
" return {\n",
" \"images\": images,\n",
" \"texts\": text\n",
" }"
" text.append(doc)\n",
" return {\"images\": images, \"texts\": text}"
]
},
{
@@ -311,6 +317,7 @@
"from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
"from langchain.schema.messages import HumanMessage, SystemMessage\n",
"\n",
"\n",
"def prompt_func(data_dict):\n",
" # Joining the context texts into a single string\n",
" formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n",
@@ -322,7 +329,7 @@
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": f\"data:image/jpeg;base64,{data_dict['context']['images'][0]}\"\n",
" }\n",
" },\n",
" }\n",
" messages.append(image_message)\n",
"\n",
@@ -342,17 +349,21 @@
" f\"User-provided keywords: {data_dict['question']}\\n\\n\"\n",
" \"Text and / or tables:\\n\"\n",
" f\"{formatted_texts}\"\n",
" )\n",
" ),\n",
" }\n",
" messages.append(text_message)\n",
"\n",
" return [HumanMessage(content=messages)]\n",
" \n",
"\n",
"\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
"\n",
"# RAG pipeline\n",
"chain = (\n",
" {\"context\": retriever | RunnableLambda(split_image_text_types), \"question\": RunnablePassthrough()}\n",
" {\n",
" \"context\": retriever | RunnableLambda(split_image_text_types),\n",
" \"question\": RunnablePassthrough(),\n",
" }\n",
" | RunnableLambda(prompt_func)\n",
" | model\n",
" | StrOutputParser()\n",
@@ -412,15 +423,16 @@
"source": [
"from IPython.display import display, HTML\n",
"\n",
"def plt_img_base64(img_base64):\n",
"\n",
"def plt_img_base64(img_base64):\n",
" # Create an HTML img tag with the base64 string as the source\n",
" image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
" \n",
"\n",
" # Display the image by rendering the HTML\n",
" display(HTML(image_html))\n",
"\n",
"docs = retriever.get_relevant_documents(\"Woman with children\",k=10)\n",
"\n",
"docs = retriever.get_relevant_documents(\"Woman with children\", k=10)\n",
"for doc in docs:\n",
" if is_base64(doc.page_content):\n",
" plt_img_base64(doc.page_content)\n",
@@ -446,9 +458,7 @@
}
],
"source": [
"chain.invoke(\n",
" \"Woman with children\"\n",
")"
"chain.invoke(\"Woman with children\")"
]
},
{

25
cookbook/qianfan_baidu_elasticesearch_RAG.ipynb
View File

@@ -82,7 +82,9 @@
"secret_access_key = \"your bos access sk\"\n",
"\n",
"# create BceClientConfiguration\n",
"config = BceClientConfiguration(credentials=BceCredentials(access_key_id, secret_access_key), endpoint = bos_host)\n",
"config = BceClientConfiguration(\n",
" credentials=BceCredentials(access_key_id, secret_access_key), endpoint=bos_host\n",
")\n",
"\n",
"loader = BaiduBOSDirectoryLoader(conf=config, bucket=\"llm-test\", prefix=\"llm/\")\n",
"documents = loader.load()\n",
@@ -109,10 +111,14 @@
"embeddings.client = sentence_transformers.SentenceTransformer(embeddings.model_name)\n",
"\n",
"db = BESVectorStore.from_documents(\n",
" documents=split_docs, embedding=embeddings, bes_url=\"your bes url\", index_name='test-index', vector_query_field='vector'\n",
" )\n",
" documents=split_docs,\n",
" embedding=embeddings,\n",
" bes_url=\"your bes url\",\n",
" index_name=\"test-index\",\n",
" vector_query_field=\"vector\",\n",
")\n",
"\n",
"db.client.indices.refresh(index='test-index')\n",
"db.client.indices.refresh(index=\"test-index\")\n",
"retriever = db.as_retriever()"
]
},
@@ -130,8 +136,15 @@
"metadata": {},
"outputs": [],
"source": [
"llm = QianfanLLMEndpoint(model=\"ERNIE-Bot\", qianfan_ak='your qianfan ak', qianfan_sk='your qianfan sk', streaming=True)\n",
"qa = RetrievalQA.from_chain_type(llm=llm, chain_type=\"refine\", retriever=retriever, return_source_documents=True)\n",
"llm = QianfanLLMEndpoint(\n",
" model=\"ERNIE-Bot\",\n",
" qianfan_ak=\"your qianfan ak\",\n",
" qianfan_sk=\"your qianfan sk\",\n",
" streaming=True,\n",
")\n",
"qa = RetrievalQA.from_chain_type(\n",
" llm=llm, chain_type=\"refine\", retriever=retriever, return_source_documents=True\n",
")\n",
"\n",
"query = \"什么是张量?\"\n",
"print(qa.run(query))"

14
docs/docs/integrations/document_loaders/docusaurus.ipynb
View File

@@ -118,7 +118,9 @@
"source": [
"loader = DocusaurusLoader(\n",
" \"https://python.langchain.com\",\n",
" filter_urls=[\"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"],\n",
" filter_urls=[\n",
" \"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"\n",
" ],\n",
")\n",
"documents = loader.load()"
]
@@ -162,9 +164,11 @@
"source": [
"loader = DocusaurusLoader(\n",
" \"https://python.langchain.com\",\n",
" filter_urls=[\"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"],\n",
" filter_urls=[\n",
" \"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"\n",
" ],\n",
" # This will only include the content that matches these tags, otherwise they will be removed\n",
" custom_html_tags=[\"#content\", \".main\"]\n",
" custom_html_tags=[\"#content\", \".main\"],\n",
")"
]
},
@@ -213,7 +217,9 @@
"source": [
"loader = DocusaurusLoader(\n",
" \"https://python.langchain.com\",\n",
" filter_urls=[\"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"],\n",
" filter_urls=[\n",
" \"https://python.langchain.com/docs/integrations/document_loaders/sitemap\"\n",
" ],\n",
" parsing_function=remove_nav_and_header_elements,\n",
")"
]

2
docs/docs/integrations/memory/neo4j_chat_message_history.ipynb
View File

@@ -25,7 +25,7 @@
" url=\"bolt://localhost:7687\",\n",
" username=\"neo4j\",\n",
" password=\"password\",\n",
" session_id=\"session_id_1\"\n",
" session_id=\"session_id_1\",\n",
")\n",
"\n",
"history.add_user_message(\"hi!\")\n",

4
docs/docs/integrations/text_embedding/fastembed.ipynb
View File

@@ -110,7 +110,9 @@
"metadata": {},
"outputs": [],
"source": [
"document_embeddings = embeddings.embed_documents([\"This is a document\", \"This is some other document\"])"
"document_embeddings = embeddings.embed_documents(\n",
" [\"This is a document\", \"This is some other document\"]\n",
")"
]
},
{

19
docs/docs/integrations/text_embedding/open_clip.ipynb
View File

@@ -48,6 +48,7 @@
"outputs": [],
"source": [
"import open_clip\n",
"\n",
"open_clip.list_pretrained()"
]
},
@@ -147,8 +148,8 @@
" \"rocket\": \"a rocket standing on a launchpad\",\n",
" \"motorcycle_right\": \"a red motorcycle standing in a garage\",\n",
" \"camera\": \"a person looking at a camera on a tripod\",\n",
" \"horse\": \"a black-and-white silhouette of a horse\", \n",
" \"coffee\": \"a cup of coffee on a saucer\"\n",
" \"horse\": \"a black-and-white silhouette of a horse\",\n",
" \"coffee\": \"a cup of coffee on a saucer\",\n",
"}\n",
"\n",
"original_images = []\n",
@@ -158,14 +159,18 @@
"plt.figure(figsize=(16, 5))\n",
"\n",
"# Loop to display and prepare images and assemble URIs\n",
"for filename in [filename for filename in os.listdir(skimage.data_dir) if filename.endswith(\".png\") or filename.endswith(\".jpg\")]:\n",
"for filename in [\n",
" filename\n",
" for filename in os.listdir(skimage.data_dir)\n",
" if filename.endswith(\".png\") or filename.endswith(\".jpg\")\n",
"]:\n",
" name = os.path.splitext(filename)[0]\n",
" if name not in descriptions:\n",
" continue\n",
"\n",
" image_path = os.path.join(skimage.data_dir, filename)\n",
" image = Image.open(image_path).convert(\"RGB\")\n",
" \n",
"\n",
" plt.subplot(2, 4, len(images) + 1)\n",
" plt.imshow(image)\n",
" plt.title(f\"{filename}\\n{descriptions[name]}\")\n",
@@ -173,7 +178,7 @@
" plt.yticks([])\n",
"\n",
" original_images.append(image)\n",
" images.append(image) # Origional code does preprocessing here\n",
" images.append(image) # Origional code does preprocessing here\n",
" texts.append(descriptions[name])\n",
" image_uris.append(image_path) # Add the image URI to the list\n",
"\n",
@@ -216,7 +221,7 @@
"# Instantiate your model\n",
"clip_embd = OpenCLIPEmbeddings()\n",
"\n",
"# Embed images and text \n",
"# Embed images and text\n",
"img_features = clip_embd.embed_image(image_uris)\n",
"text_features = clip_embd.embed_documents([\"This is \" + desc for desc in texts])\n",
"\n",
@@ -241,7 +246,7 @@
" plt.text(x, y, f\"{similarity[y, x]:.2f}\", ha=\"center\", va=\"center\", size=12)\n",
"\n",
"for side in [\"left\", \"top\", \"right\", \"bottom\"]:\n",
" plt.gca().spines[side].set_visible(False)\n",
" plt.gca().spines[side].set_visible(False)\n",
"\n",
"plt.xlim([-0.5, count - 0.5])\n",
"plt.ylim([count + 0.5, -2])\n",

7
docs/docs/integrations/vectorstores/elasticsearch.ipynb
View File

@@ -794,13 +794,18 @@
"from typing import Dict\n",
"from langchain.docstore.document import Document\n",
"\n",
"\n",
"def custom_document_builder(hit: Dict) -> Document:\n",
" src = hit.get(\"_source\", {})\n",
" return Document(\n",
" page_content=src.get(\"content\", \"Missing content!\"),\n",
" metadata={\"page_number\": src.get(\"page_number\", -1), \"original_filename\": src.get(\"original_filename\", \"Missing filename!\")},\n",
" metadata={\n",
" \"page_number\": src.get(\"page_number\", -1),\n",
" \"original_filename\": src.get(\"original_filename\", \"Missing filename!\"),\n",
" },\n",
" )\n",
"\n",
"\n",
"results = db.similarity_search(\n",
" \"What did the president say about Ketanji Brown Jackson\",\n",
" k=4,\n",

22
docs/docs/integrations/vectorstores/weaviate.ipynb
View File

@@ -149,12 +149,7 @@
"metadata": {},
"outputs": [],
"source": [
"db = Weaviate.from_documents(\n",
" docs, \n",
" embeddings, \n",
" weaviate_url=WEAVIATE_URL, \n",
" by_text=False\n",
")"
"db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)"
]
},
{
@@ -227,8 +222,7 @@
"import weaviate\n",
"\n",
"client = weaviate.Client(\n",
" url=WEAVIATE_URL, \n",
" auth_client_secret=weaviate.AuthApiKey(WEAVIATE_API_KEY)\n",
" url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(WEAVIATE_API_KEY)\n",
")\n",
"\n",
"# client = weaviate.Client(\n",
@@ -240,10 +234,7 @@
"# )\n",
"\n",
"vectorstore = Weaviate.from_documents(\n",
" documents, \n",
" embeddings, \n",
" client=client, \n",
" by_text=False\n",
" documents, embeddings, client=client, by_text=False\n",
")"
]
},
@@ -378,6 +369,7 @@
],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"llm.predict(\"What did the president say about Justice Breyer\")"
]
@@ -575,10 +567,10 @@
"from langchain.schema.output_parser import StrOutputParser\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()} \n",
" | prompt \n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser() \n",
" | StrOutputParser()\n",
")\n",
"\n",
"rag_chain.invoke(\"What did the president say about Justice Breyer\")"

11
docs/docs/modules/agents/index.ipynb
View File

@@ -198,6 +198,7 @@
"source": [
"from langchain.agents import tool\n",
"\n",
"\n",
"@tool\n",
"def get_word_length(word: str) -> int:\n",
" \"\"\"Returns the length of a word.\"\"\"\n",
@@ -606,10 +607,12 @@
"source": [
"input1 = \"how many letters in the word educa?\"\n",
"result = agent_executor.invoke({\"input\": input1, \"chat_history\": chat_history})\n",
"chat_history.extend([\n",
" HumanMessage(content=input1),\n",
" AIMessage(content=result[\"output\"]),\n",
"])\n",
"chat_history.extend(\n",
" [\n",
" HumanMessage(content=input1),\n",
" AIMessage(content=result[\"output\"]),\n",
" ]\n",
")\n",
"agent_executor.invoke({\"input\": \"is that a real word?\", \"chat_history\": chat_history})"
]
},

6
docs/scripts/copy_templates.py
View File

@@ -1,16 +1,15 @@
import glob
import os
from pathlib import Path
import re
import shutil
from pathlib import Path
TEMPLATES_DIR = Path(os.path.abspath(__file__)).parents[2] / "templates"
DOCS_TEMPLATES_DIR = Path(os.path.abspath(__file__)).parents[1] / "docs" / "templates"
readmes = list(glob.glob(str(TEMPLATES_DIR) + "/*/README.md"))
destinations = [readme[len(str(TEMPLATES_DIR)) + 1:-10] + ".md" for readme in readmes]
destinations = [readme[len(str(TEMPLATES_DIR)) + 1 : -10] + ".md" for readme in readmes]
for source, destination in zip(readmes, destinations):
full_destination = DOCS_TEMPLATES_DIR / destination
shutil.copyfile(source, full_destination)
@@ -33,4 +32,3 @@ with open(TEMPLATES_INDEX_DESTINATION, "r") as f:
content = re.sub("\]\(\.\.\/", "](/docs/templates/", content)
with open(TEMPLATES_INDEX_DESTINATION, "w") as f:
f.write(sidebar_hidden + content)

14
libs/langchain/langchain/agents/agent.py
View File

@@ -821,20 +821,6 @@ class AgentExecutor(Chain):
)
return values
@root_validator()
def validate_return_direct_tool(cls, values: Dict) -> Dict:
"""Validate that tools are compatible with agent."""
agent = values["agent"]
tools = values["tools"]
if isinstance(agent, BaseMultiActionAgent):
for tool in tools:
if tool.return_direct:
raise ValueError(
"Tools that have `return_direct=True` are not allowed "
"in multi-action agents"
)
return values
@root_validator(pre=True)
def validate_runnable_agent(cls, values: Dict) -> Dict:
"""Convert runnable to agent if passed in."""

3
templates/rag-timescale-conversation/rag_conversation.ipynb
View File

@@ -79,7 +79,6 @@
}
],
"source": [
"\n",
"answer = rag_app.invoke(\n",
" {\n",
" \"question\": \"What commits did the person with my name make?\",\n",
@@ -125,7 +124,7 @@
" \"end_date\": \"2016-01-01 00:00:00\",\n",
" }\n",
")\n",
"answer\n"
"answer"
]
},
{